Improving decision making for incentivised and weather-sensitive projects

The field of project management has originated from the domain of operational research, which focuses on the mathematical optimization of operational problems. However, in recent decades an increasingly broad perspective has been applied to the field of project management. As such, project management has spawned a number of very active sub- domains, which focus not solely on the scheduling of the project’s baseline, but also on the analysis of risk, as well as the controlling of project execution. This dissertation focuses on two areas where existing literature is still lacking. The first area is the use of incentivised contractual agreements between the owner of a project, and the contractor who is hired to execute the project. Whereas this area has received growing attention in recent years, the majority of studies remained strongly descriptive. Hence, the aim of the first part of this dissertation is to develop a more prescriptive approach from both the owner’s and the contractor’s perspective. The second part of this dissertation investigates the use of dedicated weather models to improve operational performance of weather-sensitive projects. During recent decades, significant effort has been made to improve the quality of weather simulation models. Moreover, the amount of available weather data has been steadily increasing. This opens up a lot of new possibilities for using more precise weather models in order to support operational decision making. In spite of this, the number of applications of these weather models in operational research has remained rather limited. As such, the aim of the second part of this dissertation is to leverage these weather models to improve the scheduling of offshore construction projects, as well as preventive maintenance of offshore wind turbines

20 years of DIEAP flap breast reconstruction : a big data analysis

With every hospital admission, a vast amount of data is collected from every patient. Big data can help in data mining and processing of this volume of data. The goal of this study is to investigate the potential of big data analyses by analyzing clinically relevant data from the immediate postoperative phase using big data mining techniques. A second aim is to understand the importance of different postoperative parameters. We analyzed all data generated during the admission of 739 women undergoing a free DIEAP flap breast reconstruction. The patients' complete midcare nursing report, laboratory data, operative reports and drug schedule were examined (7,405,359 data points). The duration of anesthesia does not predict the need for revision. Low Red Blood cell Counts (3.53 x 10(6)/mu L versus 3.79 x 10(6)/mu L, p < 0.001) and a low MAP (MAP = 73.37 versus 76.62; p < 0.001) postoperatively are correlated with significantly more revisions. Different drugs (asthma/COPD medication, Butyrophenones) can also play a significant role in the success of the free flap. In a world that is becoming more data driven, there is a clear need for electronic medical records which are easy to use for the practitioner, nursing staff, and the researcher. Very large datasets can be used, and big data analysis allows a relatively easy and fast interpretation all this information

Scheduling of unrelated parallel machines with limited server availability on multiple production locations: a case study in knitted fabrics

This paper studies a complex variation of the parallel machine scheduling (PMS) problem, as encountered at a Belgian producer of knitted fabrics. The aim is to assign N J jobs to N M unrelated parallel machines, minimising a weighted combination of job lateness and tardiness. Jobs are assigned specific release, and due dates and changeover times are sequence dependent. Current literature is extended by including geographically dispersed production locations, which influence job due dates and objective function coefficients. Furthermore, the changeover interference due to limited availability of technicians is also studied in this paper. The scheduling problem is solved using a hybrid meta-heuristic, which combines elements from simulated annealing and genetic algorithms. This hybrid meta-heuristic is capable of solving real-scale scheduling problems of up to 750 jobs, 75 machines and 10 production locations within reasonable computation time. This hybrid scheduling procedure is extended with heuristic dispatching rules capable of reducing the impact of changeover interference by 23% on average compared to the random scenario, for the case where a single technician is expected to serve up to 12 machines

Optimised scheduling for weather sensitive offshore construction projects

The significant lead times and costs associated with materials and equipment in combination with intrinsic and weather related variability render the planning of offshore construction projects highly complex. Moreover, the way in which scarce resources are managed has a profound impact on both the cost and the completion date of a project. Hence, schedule quality is of paramount importance to the profitability of the project. A prerequisite to the creation of good schedules is the accuracy of the procedure used to estimate the project outcome when a given schedule is used. Because of the systematic influence of weather conditions, traditional Monte Carlo simulations fail to produce a reliable estimate of the project outcomes. Hence, the first objective of this research is to improve the accuracy of the project simulation by creating a procedure which includes both uncertainty related to the activities and an integrated model of the weather conditions. The weather component has been designed to create realistically correlated wind- and weather conditions for operationally relevant time intervals. The second objective of this research is to optimise the project planning itself by using both general meta-heuristic optimisation approaches and dedicated heuristics which have been specifically designed for the problem at hand. The performance of these heuristics is judged by the expected net present value of the project. The approach presented in this paper is tested on real data from the construction of an offshore wind farm off the Belgian coast and weather data gathered by the Flanders Marine Institute using measuring poles in the North Sea

Extensions of earned value management : using the earned incentive metric to improve signal quality

This research introduces novel control metrics for projects that use cost and/or time incentives. The proposed technique extends the traditional earned value management (EV M) methodology for project control. This is done by measuring the deviation in the accrual of incentives, rather than the time and cost performance relative to the planned schedule. The proposed dedicated approach avoids two key issues when controlling incentivized projects using traditional earned value management. Firstly, the impact of variations in the cost and time dimensions are adequately weighted in the control signals. Secondly, the technique is capable of monitoring the potential non-linear accrual of incentive amounts throughout the project. The performance of the proposed technique is tested by means of a computational experiment on 4200 projects of varying size, structure and type of incentive contract. The results show that the proposed technique improves signal quality when compared to traditional EV M metrics

Incentive contract design for projects: The owner's perspective

Due to the adoption of more and more complex incentive contract structures for projects, designing the best contract for a specific situation has become an increasingly daunting task for project owners. Through the combination of findings from contracting literature with knowledge from the domain of project management, a quantitative model for the contract design problem is constructed. The contribution of this research is twofold. First of all, a comprehensive and quantitative methodology to analyse incentive contract design is introduced, based on an extensive review of the existing literature. Secondly, based on this methodology, computational experiments are carried out, which result in a set of managerial guidelines for incentive contract design. Our analysis shows that substantial improvements can often be attained by using contracts which include incentives for cost, duration as well as scope simultaneously. Moreover, nonlinear and piecewise linear formulae to calculate the incentive amounts are shown to improve both the performance and robustness across different projects

Multi-mode schedule optimisation for incentivised projects

This research presents a novel quantitative methodology to optimise the scheduling of subcontracted projects from the perspective of the contractor. Specifically, the scenario where the contractor’s remuneration is performance dependent is investigated. Based on the incentive methodology introduced by Kerkhove and Vanhoucke (2016), a novel mixed integer programming formulation as well as a greedy local search heuristic to solve the contractor’s problem are presented and tested in a computational experiment. For this experiment, a database containing 3,150 contract-project combinations with diverse structures has been created. The results from this experiment demonstrate the efficiency of the MIP formulation even for larger problem instances, as well as the influence of the project and contract structure on the contractor’s earnings